Machine learning: an advancement in biochemical engineering DOI

Ritika Saha,

Ashutosh Chauhan,

Smita Rastogi Verma

et al.

Biotechnology Letters, Journal Year: 2024, Volume and Issue: 46(4), P. 497 - 519

Published: June 20, 2024

Language: Английский

Recent trends and perspectives of artificial intelligence-based machine learning from discovery to manufacturing in biopharmaceutical industry DOI
Ravi Maharjan, Jae‐Chul Lee, Kyeong Lee

et al.

Journal of Pharmaceutical Investigation, Journal Year: 2023, Volume and Issue: 53(6), P. 803 - 826

Published: Nov. 1, 2023

Language: Английский

Citations

8
Natural and designer cellulosomes: A potential tool for enhancing microbial additive-mediated lignocellulosic agricultural waste composting DOI
Uvin Eksith Senadheera,

Dikkumburage Jasintha Jayasanka,

Dhanushka Udayanga

et al.

Bioresource Technology Reports, Journal Year: 2023, Volume and Issue: 25, P. 101695 - 101695

Published: Nov. 14, 2023

Language: Английский

Citations

7
Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability DOI
Lianpeng Sun, Mingxuan Li, Bingyou Liu

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 394, P. 130254 - 130254

Published: Dec. 25, 2023

Language: Английский

Citations

7
A machine learning‐based approach for improving plasmid DNA production in Escherichia coli fed‐batch fermentations DOI
XU Zhi-xian, Xiao‐Feng Zhu, Mohsin Ali

et al.

Biotechnology Journal, Journal Year: 2024, Volume and Issue: 19(6)

Published: June 1, 2024

Abstract Artificial Intelligence (AI) technology is spearheading a new industrial revolution, which provides ample opportunities for the transformational development of traditional fermentation processes. During plasmid fermentation, subjective process control leads to highly unstable yields. In this study, multi‐parameter correlation analysis was first performed discover dynamic metabolic balance among oxygen uptake rate, temperature, and yield, whilst revealing heating rate timing as most important optimization factor balanced cell growth production. Then, based on acquired on‐line parameters well outputs kinetic models constructed describing dynamics biomass concentration, substrate machine learning (ML) model with Random Forest (RF) best algorithm established predict optimal strategy. Finally, highest yield specific productivity 1167.74 mg L −1 8.87 /OD 600 were achieved strategy predicted by RF in 50 bioreactor, respectively, 71% 21% higher than those obtained cultures where one‐step temperature upshift applied. addition, study transformed empirical into more efficient rational self‐optimization method. The methodology employed equally applicable regulation other products, thereby facilitating potential furthering intelligent automation

Language: Английский

Citations

2
Machine learning: an advancement in biochemical engineering DOI

Ritika Saha,

Ashutosh Chauhan,

Smita Rastogi Verma

et al.

Biotechnology Letters, Journal Year: 2024, Volume and Issue: 46(4), P. 497 - 519

Published: June 20, 2024

Language: Английский

Citations

2